DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending.
This communication is in response to the communication filed December 20, 2024.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite systems, apparatuses, or methods for providing information of a potential compliance risk of a clinical trial, which are statutory categories of inventions.
Specifically, the independent claims, taking claim 1 as exemplary, recite determining…(i) a first likelihood that the investigation data is associated with the first set of one or more indicators, (ii) a second likelihood that the investigation data is associated with the second set of one or more indicators, and (iii) that the first likelihood has higher accuracy than the second likelihood; assigning a first weight to the first…model and a second weight to the second…model based on determining that the first likelihood has higher accuracy than the second likelihood, wherein the first weight is greater than the second weight; and providing…for output…an indication of the compliance risk of the clinical trial based on the first and the second weights.
The dependent claims recite the limitations of the independent claims, and further describe limitations directed towards compliance risk indications, defining the time period for reporting adverse events, combining likelihoods to determine indication of compliance risk, determining indication of the compliance risk of the clinical trial, identifying a model trained to identify indicators of compliance risk, and determining an aggregated data structure.
The limitations of the claims are interpreted as being grouped within the “mental processes” grouping of abstract ideas, because the claims involve a series of steps for collecting data, analyzing it, and outputting the results of the collection and analysis. See MPEP 2106.04. The claims are interpreted to recite concepts relating to tracking or organizing clinical trial information. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. Integration into a practical application requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Here, the additional elements of the claims use processors, computer system, training machine learning models, user interface, databases, database system, and memory.
The claims merely use the additional elements as tools to perform abstract ideas and generally link the use of a judicial exception to a particular technological environment. The use of the additional elements as tools to implement the abstract idea and generally to link the use of the abstract idea to a particular technological environment does not render the claim patent eligible, because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. Specifically, the computer system may comprise processors, machine learning models, user interface, databases, and memory functioning to store, input, output, and process data. The functions may be implemented in a high-level procedural or object oriented programming language, or machine learning language. Moreover, any computer program product or device may be used to provide machine learning instructions or data to a programmable processor (specification par. 37-40). The trained models are recited at a high level of generality and may be any trained model performing data analysis functions operating on any computer program product.
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. See Apple v. Ameranth, 842 F.3d 1229, 1240 (Fed. Cir. 2016). The additional elements do not use the exception to affect a particular treatment or prophylaxis for a disease, do not apply the exception using particular machines, and do not effect a transformation or reduction of a particular article to a different state or thing, rather the computer elements are generally stated as to their structure and function and are only used to provide information of a potential compliance risk of a clinical trial instead of directly providing specific treatment or prophylaxis. Therefore, the additional elements do not impose any meaningful limits on practicing the abstract idea and the additional limitations are not indicative of materializing into a practical application. Accordingly, the claim is directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the steps of the claims amount to no more than using computer related devices to automate or implement the abstract idea of providing information of a potential compliance risk of a clinical trial.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible.
In conclusion, the claims are directed to the abstract idea of providing information of a potential compliance risk of a clinical trial. The claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Beltre et al. US2019/0304575.
As per claim 1, Beltre teaches
a computer system implemented method for providing information of a potential compliance risk of a clinical trial, the method comprising: (Beltre abstract, fig. 1 and associated paragraphs, par. 111 teaches a computer implemented method, system, and computer program for monitoring clinical research performance, which involves collection of data related to investigation, provider, and research sites across research studies, here an example of a computing environment includes a processor, interface, memory, non transitory computer useable medium, and data sources)
training, by one or more processors of the computer system, a first machine learning model to identify a first set of one or more indicators that indicate a compliance risk of the clinical trial and training a second machine learning model to identify a second set of one or more indicators that indicate the compliance risk, wherein the first and second machine learning models are trained with investigation data collected regarding the clinical trial; (Beltre par. 3, 20, 31, 68 teaches training a machine learning model and a predictive model to determine performance scores and ranking using metrics including a performance area, attributes, and weights values for characteristics, an example is also provided for domain scores for compliance issues, the claims recite that each model is trained for the same function, therefore it is interpreted that models in the set of models may be one in the same)
determining, by the one or more processors, (i) a first likelihood that the investigation data is associated with the first set of one or more indicators, (ii) a second likelihood that the investigation data is associated with the second set of one or more indicators, and (iii) that the first likelihood has higher accuracy than the second likelihood; (Beltre fig. 3, 5, and associated paragraphs, par. 76 teaches displaying ranks, total scores, and performance scores of sites, where performance areas and attributes associated with the performance score compliance with regulatory requirements, protocol, study agreements, and contracts, here an indication of compliance risk is interpreted as any measure in a performance score that may lead one of ordinary skill in the art to conclude that that there is a compliance risk)
assigning a first weight to the first machine learning model and a second weight to the second machine learning model based on determining that the first likelihood has higher accuracy than the second likelihood, wherein the first weight is greater than the second weight; and (Beltre par. 47, 66, 72 teaches here normalization is interpreted as determining accuracy, since the machine learning may process the sub-optimal data to appropriate parameters and weighted scores for calculation. A first or second weight or score value may be higher or lower depending on the data used)
providing, by the one or more processors and for output on a user interface, an indication of the compliance risk of the clinical trial based on the first and the second weights (Beltre fig. 3, 5, and associated paragraphs, par. 76 teaches displaying ranks, total scores, and performance scores of sites, where performance areas and attributes associated with the performance score compliance with regulatory requirements, protocol, study agreements, and contracts, here an indication of compliance risk is interpreted as any measure in a performance score that may lead one of ordinary skill in the art to conclude that that there is a compliance risk).
X does not specifically teach the following limitations met by Y,
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by X to * as taught by Y with the motivation to * ().
The primary reference teaches *. The secondary reference teaches *. The difference between the references and the claimed subject matter is that the references do not disclose *. Y discloses *. Since each individual element and its function are shown in the prior art, albeit in separate references, the difference between the claimed subject matter and the prior rests not on any individual element or function but in the very combination itself.
In the substitution of using *, it is well within the capabilities of one of ordinary skill in the art to use *. Moreover, *.
In the combination of the references, it is well within the capabilities of one of ordinary skill in the art to *. One of ordinary skill in the art would have recognized that the results of the combination were predictable. Moreover, *.
As per claim 2, Beltre teaches all the limitations of claim 1 and further teach wherein the compliance risk is associated with a subset of data records identified by the set of machine learning models as representing an adverse event specified by a regulatory agency associated with the investigation data (Beltre par. 57-58 teaches metrics for the performance scoring may be based on data from Compliance History and Ethical Conduct History domains with characteristics of FDA debarment, FDA inspections, compliance issues, research conducted without Board approval, failed timely reporting to the Board, significant non-compliance, termination due to non-compliance, here the Food and Drug Administration, Institutional Review Board, and Office of Human Research Protections are interpreted as regulatory agencies associated with investigation data).
As per claim 3, Beltre teaches all the limitations of claim 2 and further teach wherein the compliance risk indicates that at least some of the data records included in the subset of data records have not been reported to the regulatory agency (Beltre par. 58 teaches failed timely reporting and research without approval, which is interpreted as not reporting data to the regulatory agencies because if there is no approval and failed timely reporting a study may not have reported any data to the agency).
As per claim 4, Beltre teaches all the limitations of claim 2 and further teach wherein the compliance risk indicates that the subset of data records are likely to have been reported to the regulatory agency within a time period greater than a threshold time period for reporting the adverse event (Beltre par. 58, 68 teaches an example of domain score searching where an investigator may have a score above a threshold, and therefore a threshold below compliance may be searched, for compliance issues including failed timely reporting).
As per claim 5, Beltre teaches all the limitations of claim 4 and further teach wherein the time period for reporting the adverse event is defined by (i) a first time point when the adverse event is discovered, and (ii) a second time point when the adverse event is reported to the regulatory agency (Beltre par. 57-58 teaches timely reporting and inspection and official action indications, FDA form 483, here, since the threshold for reporting the adverse event is the actual reporting time of the adverse event, this limitation does not appear to further narrow the definition of the threshold as described, see FDA rules and regulations and 21 CFR 312).
As per claim 6, Beltre teaches all the limitations of claim 1 and further teach combining the first likelihood and the second likelihood to determine the indication of the compliance risk of the clinical trial (Beltre fig. 5 and associated paragraphs teaches domain metrics combination used to determine a performance score for a site).
As per claim 7, Beltre teaches all the limitations of claim 6 and further teach wherein a value of the first weight exceeds a value of the second weight; and wherein combining the first likelihood and the second likelihood to determine the indication of the compliance risk of the clinical trial comprises combining the first likelihood and the second likelihood based on the first weight assigned to the first machine learning model and the second weight assigned to the second machine learning model (Beltre par. 47, 66, 72 teaches here normalization is interpreted as determining accuracy, since the machine learning may process the sub-optimal data to appropriate parameters and weighted scores for calculation. A first or second weight or score value may be higher or lower depending on the data used).
As per claim 8, Beltre teaches all the limitations of claim 1 and further teach displaying that the clinical trial has a risk-associated clinical site based on determining that a combined likelihood satisfies a threshold value by combining the first likelihood and the second likelihood based on the first and the second weights assigned to a respective machine learning model (Beltre par. 58, 68 teaches an example of domain score searching where an investigator may have a score above a threshold, and therefore a threshold below compliance may be searched, for compliance issues including failed timely reporting).
As per claim 9, Beltre teaches all the limitations of claim 1 and further teach identifying, based on one or more attributes associated with a clinical trial site of the clinical trial, a third machine learning model trained to identify, based on historical investigation data collected at the clinical trial site, one or more indicators that indicate the compliance risk of the clinical trial site (Beltre par. 3, 20, 31, 68 teaches training a machine learning model and a predictive model to determine performance scores and ranking using metrics including a performance area, attributes, and weights values for characteristics, an example is also provided for domain scores for compliance issues, the claims recite that each model is trained for the same function, therefore it is interpreted that models in the set of models may be one in the same).
As per claim 10, Beltre teaches all the limitations of claim 1 and further teach determining an aggregated data structure from the investigation data, wherein the aggregated data structure includes data fields that correspond to particular data indexes, and wherein the investigation data is retrieved from a plurality of databases of a multiple database system (Beltre par. 40, 113, 117 teaches database systems implementing various data structures. A knowledge store creates an ontology structure which includes various entities and relationships that will be required by a site optimizer.).
As per claims 11-20, Beltre teaches all the claim limitations as per claims 1-10.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY M. PATEL whose telephone number is (571)272-6793 and email is jay.patel2@uspto.gov. The examiner can normally be reached on Monday-Friday 8AM-4:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H. Choi can be reached on (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAY M. PATEL/Primary Examiner, Art Unit 3686
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